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Background: Insulin resistance (IR) is a pivotal pathogenesis characteristic of type 2 diabetes mellitus (T2DM). The current study aimed to explore the association between triglyceride/high-density lipoprotein cholesterol ratio (TG/HDL-c), triglyceride–glucose (TyG), and triglyceride glucose–body mass index (TyG–BMI), and T2DM incidence. Methods: A total of 116,855 Chinese adults aged over 20 without diabetes were included. Multivariate Cox regression analysis and restricted cubic spine were utilized to investigate the association between IR indicators and T2DM. The T2DM risk across different quartiles of IR parameters was compared using Kaplan–Meier curves. The receiver operating characteristic analysis was used to investigate the predictive potential of each IR indicator for future T2DM. Results: A total of 2685 participants developed T2DM during a median follow-up of 2.98 years. The adjusted hazard ratios (HR) of incident T2DM were 1.177, 2.766, and 1.1018 for TG/HDL-c, TyG, and TyG–BMI, respectively. There were significant increasing trends of T2DM across the quartiles of TG/HDL-c, TyG, and TyG–BMI. The HRs of new-onset T2DM in the highest quartiles versus the lowest quartile of TG/HDL-c, TyG, and TyG–BMI were 3.298, 8.402, and 8.468. RCS revealed the nonlinear relationship between IR and T2DM risk. The correlations between IR and T2DM were more pronounced in subjects aged <40. TyG–BMI had the highest predictive value for incident T2DM (AUC = 0.774), with a cut-off value of 213.289. Conclusion: TG/HDL-c, TyG, and TyG–BMI index were all significantly positively associated with higher risk for future T2DM. Baseline TyG–BMI level had high predictive value for the identification of T2DM. [ABSTRACT FROM AUTHOR]

Objective: To explore the frequency and effect of extreme temperature on the non-accidental death rate in Hulunbuir, a Chinese ice city. Methods: From 2014 to 2018, mortality data of residents residing in Hulunbuir City were collected. The lag and cumulative effects of extreme temperature conditions on non-accidental death and respiratory and circulatory diseases were analyzed by distributed lag non-linear models (DLNM). Results: The risk of death was the highest during high-temperature conditions, the RR value was 1.111 (95% CI 1.031 ~ 1.198). The effect was severe and acute. The risk of death during extreme low-temperature conditions peaked on the fifth day, (RR 1.057; 95% CI 1.012 ~ 1.112), then decreased and was maintained for 12 days. The cumulative RR value was 1.289 (95% CI 1.045 ~ 1.589). Heat significantly influenced the incidence of non-accidental death in both men (RR 1.187; 95% CI 1.059–1.331) and women (RR 1.252; 95% CI 1.085–1.445). Conclusions: Regardless of the temperature effect, the risk of death in the elderly group (≥ 65 years) was significantly higher than that of the young group (0–64 years). High-temperature and low-temperature conditions can contribute to the increased number of deaths in Hulunbei. While high-temperature has an acute effect, low-temperature has a lagging effect. Elderly and women, as well as people with circulatory diseases, are more sensitive to extreme temperatures. [ABSTRACT FROM AUTHOR]

The myriad sensing nodes in the Internet of Things (IoT) are mainly powered by battery, which has limited the lifespan and increased the maintenance costs. Herein, a self-powered IoT sensing node based on triboelectric nanogenerator (TENG) is proposed for the sustainable environmental monitoring. The wind powered TENG (W-TENG) is adopted in freestanding mode with the rabbit hair and six pairs of finger electrodes. With the energy management module, the weak electrical energy from W-TENG can be converted into a stable direct current (DC) 2.5 V voltage for the operation of the IoT sensing node. When the storage energy exceeds 4.4 V, the node can be activated, then the microprogrammed control unit (MCU) transmits the monitoring data. Thereafter, the monitoring data will be identified and relayed to the IoT cloud platform by narrowband IoT (NB-IoT) module. At a wind speed of 8.4 m/s, the node can realize the wireless monitoring and data transmission for temperature and atmosphere pressure every 30 s. This work has provided a universal strategy for sustainable IoT sensing nodes powered by environmental micro-nano mechanical energy and exhibited potential applications in IoT, big data, and environmental monitoring. [ABSTRACT FROM AUTHOR]